OpenRaiser/Intern-Atlas huggingface dataset:
Effect of Hotspot Traffic on Blocking Probability in Elastic Optical Networks
Venue: arXiv cs.NI 2025
Year: 2025

In a circuit-switched network, traffic can be characterized by several factors that define how communication resources are allocated and utilized during a connection. The amount of traffic basically determines how frequently connection requests arrive, how long the setup connection remains active, and the bandwidth used. The Poisson Arrival Process models traffic arrival events at random intervals. It assumes that events happen independently of one another. This model is ideal for simulating traffic in networks where arrivals happen independently and randomly, such as the start of phone calls, data requests, or packet transmissions. The Poisson Arrival Process and uniformly choosing source and destination pair is been used most commonly by researchers to generate traffic in a network to test various promising routing and spectrum assignment algorithms. It checks the algorithm in uniformly loaded conditions and estimate its baseline performance. In real real-world scenario, a bunch of network nodes can start experiencing heavy data traffic compared to the rest of the network. This can lead to latency issues, or even outages if the network is not optimized to handle the load at these nodes which are also called hotspots. In other terms, hotspot in a network is an area or set of nodes within the network that have a higher likelihood of being involved in communication or data transmission compared to other areas. In this paper, we have tried to find what are the various factors involved in increasing the blocking probability in hotspot traffic scenarios. We have also compared the results with the uniform traffic load conditions in same topology.
Fast Autoregressive Video Generation with Diagonal Decoding
Venue: arXiv cs.CV 2025
Year: 2025

Autoregressive Transformer models have demonstrated impressive performance in video generation, but their sequential token-by-token decoding process poses a major bottleneck, particularly for long videos represented by tens of thousands of tokens. In this paper, we propose Diagonal Decoding (DiagD), a training-free inference acceleration algorithm for autoregressively pre-trained models that exploits spatial and temporal correlations in videos. Our method generates tokens along diagonal paths in the spatial-temporal token grid, enabling parallel decoding within each frame as well as partially overlapping across consecutive frames. The proposed algorithm is versatile and adaptive to various generative models and tasks, while providing flexible control over the trade-off between inference speed and visual quality. Furthermore, we propose a cost-effective finetuning strategy that aligns the attention patterns of the model with our decoding order, further mitigating the training-inference gap on small-scale models. Experiments on multiple autoregressive video generation models and datasets demonstrate that DiagD achieves up to $10\times$ speedup compared to naive sequential decoding, while maintaining comparable visual fidelity.
Towards properties of adversarial image perturbations
Venue: arXiv cs.CV 2025
Year: 2025

Using stochastic gradient approach we study the properties of adversarial perturbations resulting in noticeable growth of VMAF image quality metric. The structure of the perturbations is investigated depending on the acceptable PSNR values and based on the Fourier power spectrum computations for the perturbations. It is demonstrated that moderate variation of image brightness ($\sim 10$ pixel units in a restricted region of an image can result in VMAF growth by $\sim 60\%$). Unlike some other methods demonstrating similar VMAF growth, the subjective quality of an image remains almost unchanged. It is also shown that the adversarial perturbations may demonstrate approximately linear dependence of perturbation amplitudes on the image brightness. The perturbations are studied based on the direct VMAF optimization in PyTorch. The significant discrepancies between the metric values and subjective judgements are also demonstrated when image restoration from noise is carried out using the same direct VMAF optimization.
Inference-Time Intervention in Large Language Models for Reliable Requirement Verification
Venue: arXiv cs.AI 2025
Year: 2025

Steering the behavior of Large Language Models (LLMs) remains a challenge, particularly in engineering applications where precision and reliability are critical. While fine-tuning and prompting methods can modify model behavior, they lack the dynamic and exact control necessary for engineering applications. Inference-time intervention techniques provide a promising alternative, allowing targeted adjustments to LLM outputs. In this work, we demonstrate how interventions enable fine-grained control for automating the usually time-intensive requirement verification process in Model-Based Systems Engineering (MBSE). Using two early-stage Capella SysML models of space missions with associated requirements, we apply the intervened LLMs to reason over a graph representation of the model to determine whether a requirement is fulfilled. Our method achieves robust and reliable outputs, significantly improving over both a baseline model and a fine-tuning approach. By identifying and modifying as few as one to three specialised attention heads, we can significantly change the model's behavior. When combined with self-consistency, this allows us to achieve perfect precision on our holdout test set.
Exploring Stress among International College Students in China
Venue: arXiv cs.HC 2025
Year: 2025

Psychological stress encompasses emotional tension and pressure experienced by people, which usually arises from situations people find challenging. However, more is needed to know about the pressures faced by international college students studying in China. The goal of this study is to investigate the various stressors that international college students in China face and how they cope with stress (coping mechanisms). Twenty international students were interviewed to gather data, which was then transcribed. Thematic analysis and coding were applied to the qualitative data, revealing themes related to the causes of stress. The following themes emerge from this data: anticipatory anxiety or future stress, social and cultural challenges, financial strain, and academic pressure. These themes will help understand the various stressors international college students in China face and how they try to cope. Studying how international college students in China cope with challenges can guide the development of targeted interventions to support their mental health. Research suggests that integrating aesthetics and connectivity into design interventions can notably improve the well-being of these students. This paper presents possible future design solutions, leveraging the aesthetics of connectivity to empower students and enhance their resilience. Additionally, it aims to provide valuable insights for designers interested in creating solutions that alleviate stress and promote emotional awareness among international students.
Concat-ID: Towards Universal Identity-Preserving Video Synthesis
Venue: arXiv cs.CV 2025
Year: 2025

We present Concat-ID, a unified framework for identity-preserving video generation. Concat-ID employs variational autoencoders to extract image features, which are then concatenated with video latents along the sequence dimension. It relies exclusively on inherent 3D self-attention mechanisms to incorporate them, eliminating the need for additional parameters or modules. A novel cross-video pairing strategy and a multi-stage training regimen are introduced to balance identity consistency and facial editability while enhancing video naturalness. Extensive experiments demonstrate Concat-ID's superiority over existing methods in both single and multi-identity generation, as well as its seamless scalability to multi-subject scenarios, including virtual try-on and background-controllable generation. Concat-ID establishes a new benchmark for identity-preserving video synthesis, providing a versatile and scalable solution for a wide range of applications.
What elements should we focus when designing immersive virtual nature? A preliminary user study
Venue: arXiv cs.HC 2025
Year: 2025

Extensive research has confirmed the positive relationship between exposure to natural environments and human cognitive, behavioral, physical, and mental health. However, only some have easy access to nature. With electronic information and simulation technology advancements, digital nature experiences are widely used across various devices and scenarios. It is essential to explore how to effectively select and utilize natural elements to guide the design of digital nature scenes. This paper examines critical elements in immersive virtual nature (IVN) and their impact on user perception. Through online surveys and design experiments, we identified specific natural elements that promote relaxation and proposed design strategies for virtual environments. We developed several immersive virtual nature scenes for further validation. Finally, we outline our future experimental plans and research directions in digital nature. Our research aims to provide HCI designers insights into creating restorative, immersive virtual scenes.
NERCat: Fine-Tuning for Enhanced Named Entity Recognition in Catalan
Venue: arXiv cs.CL 2025
Year: 2025

Named Entity Recognition (NER) is a critical component of Natural Language Processing (NLP) for extracting structured information from unstructured text. However, for low-resource languages like Catalan, the performance of NER systems often suffers due to the lack of high-quality annotated datasets. This paper introduces NERCat, a fine-tuned version of the GLiNER[1] model, designed to improve NER performance specifically for Catalan text. We used a dataset of manually annotated Catalan television transcriptions to train and fine-tune the model, focusing on domains such as politics, sports, and culture. The evaluation results show significant improvements in precision, recall, and F1-score, particularly for underrepresented named entity categories such as Law, Product, and Facility. This study demonstrates the effectiveness of domain-specific fine-tuning in low-resource languages and highlights the potential for enhancing Catalan NLP applications through manual annotation and high-quality datasets.
5G-Enabled Teleoperated Driving: An Experimental Evaluation
Venue: arXiv cs.ET 2025
Year: 2025

Teleoperated driving enables remote human intervention in autonomous vehicles, addressing challenges in complex driving environments. However, its effectiveness depends on ultra-low latency, high-reliability communication. This paper evaluates teleoperated driving over 5G networks, analyzing key performance metrics such as glass-to-glass (G2G) latency, RTT and steering command delay. Using a real-world testbed with a Kia Soul EV and a remote teleoperation platform, we assess the feasibility and limitations of 5G-enabled teleoperated driving. Our system achieved an average G2G latency of 202ms and an RTT of 47ms highlighting the G2G latency as the critical bottleneck. The steering control proved to be mostly accurate and responsive. Finally, this paper provides recommendations and outlines future work to improve future teleoperated driving deployments for safer and more reliable autonomous mobility.
Inferring Events from Time Series using Language Models
Venue: arXiv cs.AI 2025
Year: 2025

Time series data measure how environments change over time and drive decision-making in critical domains like finance and healthcare. A common goal in analyzing time series data is to understand the underlying events that cause the observed variations. We conduct the first study of whether Large Language Models (LLMs) can infer events described with natural language from time series data. We evaluate 18 LLMs on a task to match event sequences with real-valued time series data using a new benchmark we develop using sports data. Several current LLMs demonstrate promising abilities, with OpenAI's o1 performing the best but with DS-R1-distill-Qwen-32B outperforming proprietary models such as GPT-4o. From insights derived from analyzing reasoning failures, we also find clear avenues to improve performance. By applying post-training optimizations, i.e., distillation and self-improvement, we significantly enhance the performance of the Qwen2.5 1.5B, achieving results second only to o1. All resources needed to reproduce our work are available: https://github.com/BennyTMT/GAMETime
Driving behavior recognition via self-discovery learning
Venue: arXiv cs.AI 2025
Year: 2025

Autonomous driving systems require a deep understanding of human driving behaviors to achieve higher intelligence and safety.Despite advancements in deep learning, challenges such as long-tail distribution due to scarce samples and confusion from similar behaviors hinder effective driving behavior detection.Existing methods often fail to address sample confusion adequately, as datasets frequently contain ambiguous samples that obscure unique semantic information.
Layer-wise Adaptive Gradient Norm Penalizing Method for Efficient and Accurate Deep Learning
Venue: arXiv cs.LG 2025
Year: 2025

Sharpness-aware minimization (SAM) is known to improve the generalization performance of neural networks. However, it is not widely used in real-world applications yet due to its expensive model perturbation cost. A few variants of SAM have been proposed to tackle such an issue, but they commonly do not alleviate the cost noticeably. In this paper, we propose a lightweight layer-wise gradient norm penalizing method that tackles the expensive computational cost of SAM while maintaining its superior generalization performance. Our study empirically proves that the gradient norm of the whole model can be effectively suppressed by penalizing the gradient norm of only a few critical layers. We also theoretically show that such a partial model perturbation does not harm the convergence rate of SAM, allowing them to be safely adapted in real-world applications. To demonstrate the efficacy of the proposed method, we perform extensive experiments comparing the proposed method to mini-batch SGD and the conventional SAM using representative computer vision and language modeling benchmarks.
Rolling Forward: Enhancing LightGCN with Causal Graph Convolution for Credit Bond Recommendation
Venue: arXiv cs.IR 2025
Year: 2025

Graph Neural Networks have significantly advanced research in recommender systems over the past few years. These methods typically capture global interests using aggregated past interactions and rely on static embeddings of users and items over extended periods of time. While effective in some domains, these methods fall short in many real-world scenarios, especially in finance, where user interests and item popularity evolve rapidly over time. To address these challenges, we introduce a novel extension to Light Graph Convolutional Network (LightGCN) designed to learn temporal node embeddings that capture dynamic interests. Our approach employs causal convolution to maintain a forward-looking model architecture. By preserving the chronological order of user-item interactions and introducing a dynamic update mechanism for embeddings through a sliding window, the proposed model generates well-timed and contextually relevant recommendations. Extensive experiments on a real-world dataset from BNP Paribas demonstrate that our approach significantly enhances the performance of LightGCN while maintaining the simplicity and efficiency of its architecture. Our findings provide new insights into designing graph-based recommender systems in time-sensitive applications, particularly for financial product recommendations.
CRCE: Coreference-Retention Concept Erasure in Text-to-Image Diffusion Models
Venue: arXiv cs.CV 2025
Year: 2025

Text-to-Image diffusion models can produce undesirable content that necessitates concept erasure. However, existing methods struggle with under-erasure, leaving residual traces of targeted concepts, or over-erasure, mistakenly eliminating unrelated but visually similar concepts. To address these limitations, we introduce CRCE, a novel concept erasure framework that leverages Large Language Models to identify both semantically related concepts that should be erased alongside the target and distinct concepts that should be preserved. By explicitly modelling coreferential and retained concepts semantically, CRCE enables more precise concept removal, without unintended erasure. Experiments demonstrate that CRCE outperforms existing methods on diverse erasure tasks, including real-world object, person identities, and abstract intellectual property characteristics. The constructed dataset CorefConcept and the source code will be release upon acceptance.
Beyond Single Pass, Looping Through Time: KG-IRAG with Iterative Knowledge Retrieval
Venue: arXiv cs.AI 2025
Year: 2025

Graph Retrieval-Augmented Generation (GraphRAG) has proven highly effective in enhancing the performance of Large Language Models (LLMs) on tasks that require external knowledge. By leveraging Knowledge Graphs (KGs), GraphRAG improves information retrieval for complex reasoning tasks, providing more precise and comprehensive retrieval and generating more accurate responses to QAs. However, most RAG methods fall short in addressing multi-step reasoning, particularly when both information extraction and inference are necessary. To address this limitation, this paper presents Knowledge Graph-Based Iterative Retrieval-Augmented Generation (KG-IRAG), a novel framework that integrates KGs with iterative reasoning to improve LLMs' ability to handle queries involving temporal and logical dependencies. Through iterative retrieval steps, KG-IRAG incrementally gathers relevant data from external KGs, enabling step-by-step reasoning. The proposed approach is particularly suited for scenarios where reasoning is required alongside dynamic temporal data extraction, such as determining optimal travel times based on weather conditions or traffic patterns. Experimental results show that KG-IRAG improves accuracy in complex reasoning tasks by effectively integrating external knowledge with iterative, logic-based retrieval. Additionally, three new datasets: weatherQA-Irish, weatherQA-Sydney, and trafficQA-TFNSW, are formed to evaluate KG-IRAG's performance, demonstrating its potential beyond traditional RAG applications.
Joint Channel Bandwidth Assignment and Relay Positioning for Predictive Flying Networks
Venue: arXiv cs.NI 2025
Year: 2025

Flying Networks (FNs) have emerged as a promising solution to provide on-demand wireless connectivity when network coverage is insufficient or the communications infrastructure is compromised, such as in disaster management scenarios. Despite extensive research on Unmanned Aerial Vehicle (UAV) positioning and radio resource allocation, the challenge of ensuring reliable traffic relay through backhaul links in predictive FNs remains unexplored. This work proposes Simulated Annealing for predictive FNs (SAFnet), an innovative algorithm that optimizes network performance under positioning constraints, limited bandwidth and minimum rate requirements. Our algorithm uniquely leverages prior knowledge of the first-tier node trajectories to assign bandwidth and dynamically adjust the position of the second-tier flying relay. Building upon Simulated Annealing, our approach enhances this well-known AI algorithm with penalty functions, achieving performance levels comparable to exhaustive search while significantly reducing computational complexity.
JuDGE: Benchmarking Judgment Document Generation for Chinese Legal System
Venue: arXiv cs.CL 2025
Year: 2025

This paper introduces JuDGE (Judgment Document Generation Evaluation), a novel benchmark for evaluating the performance of judgment document generation in the Chinese legal system. We define the task as generating a complete legal judgment document from the given factual description of the case. To facilitate this benchmark, we construct a comprehensive dataset consisting of factual descriptions from real legal cases, paired with their corresponding full judgment documents, which serve as the ground truth for evaluating the quality of generated documents. This dataset is further augmented by two external legal corpora that provide additional legal knowledge for the task: one comprising statutes and regulations, and the other consisting of a large collection of past judgment documents. In collaboration with legal professionals, we establish a comprehensive automated evaluation framework to assess the quality of generated judgment documents across various dimensions. We evaluate various baseline approaches, including few-shot in-context learning, fine-tuning, and a multi-source retrieval-augmented generation (RAG) approach, using both general and legal-domain LLMs. The experimental results demonstrate that, while RAG approaches can effectively improve performance in this task, there is still substantial room for further improvement. All the codes and datasets are available at: https://github.com/oneal2000/JuDGE.
Positivity Proofs for Linear Recurrences with Several Dominant Eigenvalues
Venue: arXiv cs.SC 2025
Year: 2025

Deciding the positivity of a sequence defined by a linear recurrence and initial conditions is, in general, a hard problem. When the coefficients of the recurrences are constants, decidability has only been proven up to order 5. The difficulty arises when the characteristic polynomial of the recurrence has several roots of maximal modulus, called dominant roots of the recurrence. We study the positivity problem for recurrences with polynomial coefficients, focusing on sequences of Poincaré type, which are perturbations of constant-coefficient recurrences. The dominant eigenvalues of a recurrence in this class are the dominant roots of the associated constant-coefficient recurrence. Previously, we have proved the decidability of positivity for recurrences having a unique, simple, dominant eigenvalue, under a genericity assumption. The associated algorithm proves positivity by constructing a positive cone contracted by the recurrence operator. We extend this cone-based approach to a larger class of recurrences, where a contracted cone may no longer exist. The main idea is to construct a sequence of cones. Each cone in this sequence is mapped by the recurrence operator to the next. This construction can be applied to prove positivity by induction. For recurrences with several simple dominant eigenvalues, we provide a condition that ensures that these successive inclusions hold. Additionally, we demonstrate the applicability of our method through examples, including recurrences with a double dominant eigenvalue.
EmotionCarrier: A Multimodality 'Mindfulness-Training' Tool for Positive Emotional Value
Venue: arXiv cs.HC 2025
Year: 2025

This study introduced a Multimodal Mindfulness-Training System. Our installation, 'EmotionCarrier', correlates traditional calligraphy interactions with real-time physiological data from an Apple Watch. We aim to enhance mindfulness training effectiveness, aiding in achieving physiological calmness through calligraphy practice. Our experiments with varied participant groups focused on data diversity, usability, and stability. We adopted methods like using EmotionCarrier for Heart Sutra transcription and adjusting installation placement for optimal user experience. Our primary finding was a correlation between calligraphy performance data and emotional responses during the transcription of the Heart Sutra.